---
title: Using Datasets for Fine-tuning
description: Export Latitude datasets to prepare data for fine-tuning language models.
---

While Latitude focuses on prompt engineering and evaluation, the datasets you create and curate within the platform can be valuable assets for fine-tuning language models using external tools and services.

## Why Use Latitude Datasets for Fine-tuning?

- **Curated Data**: Datasets often contain carefully selected inputs and high-quality outputs (either expected outputs or actual model responses reviewed manually).
- **Real-World Examples**: Datasets created from production logs represent actual user interactions.
- **Structured Format**: Latitude datasets are already in a structured format (CSV), making them easier to process for fine-tuning.

## Exporting Datasets from Latitude

1.  Navigate to the "Datasets" section in your project.
2.  Locate the dataset you want to use for fine-tuning.
3.  Find the option to **Download** or **Export** the dataset (usually represented by a download icon).
4.  Save the resulting CSV file to your local machine.

## Preparing Data for Fine-tuning

Once exported, you'll likely need to transform the CSV data into the specific format required by your chosen fine-tuning platform or library (e.g., OpenAI's JSONL format, Hugging Face datasets format).

Common steps include:

1.  **Selecting Columns**: Identify the columns containing the input prompt/context and the desired completion/output.
2.  **Formatting**: Convert each row into the required structure. For example, for OpenAI fine-tuning, you might create JSON objects like:
    ```json
    {"prompt": "<Input from CSV column A>", "completion": "<Output from CSV column B>"}
    // or for chat models:
    {"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "<Input>"}, {"role": "assistant", "content": "<Output>"}]}
    ```
3.  **Data Cleaning**: Review the data for quality, consistency, and remove any low-quality or irrelevant examples.
4.  **Splitting Data**: You might need to split your exported dataset into training and validation sets.

Consult the documentation of your specific fine-tuning tool or platform for detailed formatting requirements.

## Example Scenario

Imagine you have a Latitude dataset created from manually reviewed chat logs (`input_query`, `high_quality_response`).

1.  **Export**: Download this dataset as a CSV from Latitude.
2.  **Transform**: Write a script (e.g., Python with pandas) to read the CSV and convert each row into the JSONL format required by the fine-tuning API you plan to use.
3.  **Fine-tune**: Upload the formatted JSONL file and run the fine-tuning job using the provider's tools.
4.  **Evaluate**: After fine-tuning, you can even evaluate the new model's performance back in Latitude by configuring it as a new provider/model and running evaluations against your datasets.

By leveraging the data curation work done in Latitude, you can streamline the preparation process for fine-tuning models for specialized tasks.

## Next Steps

- Learn about [Creating and Using Datasets](/guides/datasets/overview) in Latitude.
- Refer to external documentation for specific fine-tuning platforms (OpenAI, Hugging Face, etc.).
